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RESEARCH AND PRACTICE |
Daniel J. Foley, Jack M. Guralnik, and Dwight B. Brock are with the Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, Md. Harley K. Heimovitz is with Sytel Inc, Rockville, Md.
Correspondence: Requests for reprints should be sent to Daniel J. Foley, MS, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, 7201 Wisconsin Ave, Bethesda, MD 20892 (e-mail: foleyd{at}gw.nia.nih.gov).
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Objectives. We estimated total life expectancy and driving life expectancy of US drivers aged 70 years and older.
Methods. Life table methods were applied to 4699 elderly persons who were driving in 1993 and reassessed in a 1995 survey.
Results. Drivers aged 70 to 74 years had a driving life expectancy of approximately 11 years. A higher risk of mortality among men as a cause of driving cessation offset a higher risk of driving cessation not related to mortality among women that resulted in similar driving life expectancies.
Conclusions. Nationwide, many elderly drivers quit driving each year and must seek alternative sources of transportation. Because of differences in life expectancy, women require more years of support for transportation, on average, than men after age 70.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Compared with middle-aged drivers, older drivers have about a 3-fold increased risk of crashing per mile driven. However, older persons drive markedly fewer miles annually than middle-aged drivers, resulting in an equivalent annualized risk for crashing.4 Consequently, older drivers, on average, have little increase in their annual cost of automobile insurance during their transition from middle age. In contrast, teenaged drivers, on average, have a very high risk of crashing on an annual basis, regardless of miles driven, and pay much higher premiums for their automobile insurance. The greater threat to an older driver is the risk of fatality from an automobile crash. Although the annual risk of crashing remains fairly stable over the years of driving, the risk of dying after involvement in an automobile crash increases significantly with age. Compared with middle-aged drivers of the same sex and involved in the same severity of crash, older drivers are 3 times more likely to die as a result of the crash.5 Nearly 5000 drivers aged 70 years and older were involved in fatal crashes in 1999, a 42% increase in the number over the preceding decade.6 This trend for an increasing number of elderly driver fatalities is expected to continue as long as the proportion of all drivers who are aged 70 years and older continues the rise observed over the past few years—for example, from 8% in 1989 to 9% in 1999.
In general, older drivers decide for themselves when to quit, a decision that often stems from the onset and progression of medical conditions that affect visual, physical, and cognitive functioning and consequently driving skill.7–10 In addition, studies show that cessation is not an easy decision and may have consequences such as depressed mood and less social engagement due to loss of mobility.11,12 The role of health professionals in assisting with this decision continues to be discussed, as does the role of state policies for license renewal.13,14 Of paramount concern to the older driver who is pondering cessation is the availability and cost of alternative sources of transportation.15 Such sources may include formal services such as public transportation systems, taxis, and community-sponsored or church-sponsored van services. More informal support typically comes from family and friends who live nearby and can drive.
Few epidemiological studies have addressed the implications of this transition to dependency on others for transportation among elderly drivers in the context of public health planning and provision of services. We used data from a longitudinal study of aging in a nationally representative sample of older adults to estimate both total life expectancy and driving life expectancy, which can be used to project the number of years of life, on average, in which older persons will be dependent on alternative sources of transportation. In addition, we estimated the number of elderly persons nationwide who do not drive and the number who quit driving each year, and we assessed the effect of visual, physical, and cognitive impairments on their risk for driving cessation.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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Interviews were conducted with the ageeligible household head and that respondent’s spouse or partner. A few spouses were younger than 70 years and were excluded from the analyses. Persons younger than 80 years in the Asset and Health Dynamics Among the Oldest Old study were interviewed preferably by telephone, and those aged 80 years and older were interviewed preferably in person. About 10% of the interviews were conducted with a proxy for the eligible participant, and about 70% were completed in the preferred mode. On average, the baseline interview lasted approximately 1 hour and covered 10 subject areas, including demographics, health, cognition, family structure, use and cost of health services, job status, income, net worth, subjective expectations of assets and health, and insurance coverage. A total of 8222 persons participated in the 1993 baseline interview for the Asset and Health Dynamics Among the Oldest Old study (80% of the eligible sample), and 7447 were aged 70 years or older.
At baseline, participants were asked whether they were able to drive and whether they had a car available to use when they needed one. Those who were 70 years of age or older and were able to drive and had a car available (n = 4699) were included in the follow-up analysis of mortality (n = 338) and driving cessation (n = 387). About 5% of the drivers survived but refused to participate in the follow-up survey or were unable to be located in follow-up (n = 238). Weights for driving time over the follow-up interval were assigned on the basis of the respondent’s outcome and in accordance with standard life table methods: persons who quit driving over the 2-year interval were assigned 1.0 year of driving, persons who died between waves were assumed to have survived 1 year and driven 0.5 years, and those who refused to participate or could not be located were assigned 1.85 years of driving, the average for the cohort (including 2.0 years for those who were still driving at follow-up).
In addition to their baseline age group (70–74, 75–79, 80–84, and 85 years or older) and sex, drivers were classified according to whether they had poor vision, limitations in activities of daily living (ADLs), memory impairment, or depressed mood. Poor vision was based on a self- or proxy report of having fair or poor vision status. Persons who reported having difficulty in any of 6 tasks (walking, dressing, eating, bathing, toileting, or getting into or out of bed) or who were unable to do any of the tasks were categorized as having an ADL limitation.17 Persons who could not recall 3 or more words after a 5-minute delay in a free recall of 10 short and concrete words were classified as having poor memory. Those participating by proxy were classified as having poor memory if the proxy reported that the subject had a poor memory status.18 Persons with depressed mood included those with a score of 3 or greater on an 8-item abbreviated version of the 20-item Center for Epidemiological Studies Depression Scale.19,20 A variable indicating "proxy participation" was used to represent the proxy respondents when no Center for Epidemiological Studies Depression Scale score was available.
Statistical Methods
In the analyses, weighted data with standard errors of the estimates were used to adjust for the complexities of the sample design. Estimates of population percentages and totals were obtained from the software package SUDAAN, which provides appropriate estimates of the standard errors of those statistics with a Taylor series approximation.21 Similarly, SUDAAN was used for fitting multiple logistic regression models of driving cessation that also properly accounted for the sample design with weighted data.
Standard current abridged life tables were computed on the basis of weighted estimates of survival probabilities, resulting in appropriately adjusted estimates of life expectancy and driving life expectancy.22,23 Because SUDAAN does not provide a procedure for estimating standard errors of life table functions, the standard errors of the life expectancy estimates were calculated with a statistical method (i.e., a jackknife estimator) that properly accounted for the sample design.24 The accuracy of the jackknife estimator was successfully assessed by calculating standard errors of percentages that could be compared with estimates of the same characteristics obtained with SUDAAN.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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). Overall, 82% of the men aged 70 years or older were driving, compared with only 55% of the women. As expected, the prevalence of driving declined sharply with increasing age, ranging from 88% of men in their early 70s to 55% of those aged 85 years or older. Among women, the prevalence of driving ranged from 70% among those aged 70 to 74 years of age to 22% among those aged 85 years or older.
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). About 5% of the baseline cohort refused to participate in the follow-up study or could not be located at follow-up. In general, mortality and driving cessation rates increased among the older age groups, in contrast to the refusal and lost to follow-up rates, which did not vary significantly by age group or sex. Overall, the mortality rate among male drivers (89 deaths per 1000 drivers) was 62% higher than that among female drivers (55 deaths per 1000 drivers). In contrast, the driving cessation rate among female drivers (112 per 1000 drivers) was 78% higher than the rate among male drivers (63 per 1000 drivers). The offsetting risks for mortality and driving cessation between men and women resulted in an equivalent proportion who continued to drive after 2 years of follow-up, ranging from approximately 90% among those aged 70 to 74 years to about 60% among those aged 85 years and older (Figure 1). These 2-year transition rates indicate that each year, more than 600 000 persons aged 70 years and older nationwide stop driving and become dependent on others to meet their transportation needs, whereas another 400 000 of these older drivers will die within the year.
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, last column). In contrast, although the total life expectancy for women aged 70 to 74 years would be approximately 21 years, their driving life expectancy would be similar to men’s, only 11 years. These findings suggest that, on average, male drivers aged 70 to 74 years will be dependent on alternative sources of transportation for approximately 7 years and that female drivers of the same age will be dependent on alternative sources of transportation for approximately 10 years. Among the oldest old drivers in this cohort (those aged 85 years and older), driving life expectancy was approximately 2 years, and this remaining period of driving covered about one-third of the total life expectancy for men and about one-fourth of the total life expectancy for women. Stratification of the drivers by standard metropolitan statistical area residence resulted in similar estimates of driving life expectancy.
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). Although women were 3 times more likely than men to quit driving, the single most important factor determining who quit driving was participation by proxy in the baseline survey. These drivers had a 10-fold increased risk of driving cessation compared with self-reporting drivers. In comparison with drivers reporting no vision, ADL, or memory problems or those reporting only 1 of these problems at baseline but not at follow-up (i.e., the reference group), drivers who reported 1 or more of these problems at follow-up but not at baseline (incident problems) had significantly higher odds ratios for driving cessation. Furthermore, drivers who reported these problems at both baseline and follow-up (chronic problems) had the highest odds ratios for no longer driving. In contrast, incident depressed mood was more strongly associated with driving cessation than was chronic depressed mood. This finding may reflect the possibility that persons who quit driving during the 2-year period of follow-up were more likely to report symptoms of depression at the time of their participation in the follow-up survey than were those who continued to drive.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONReferences |
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The Asset and Health Dynamics Among the Oldest Old longitudinal study data also showed that male and female drivers aged 70 to 74 years had equivalent driving life expectancies of approximately 11 years. This figure resulted from the finding that men and women had an equivalent probability of driving cessation after mortality differences in their risks for cessation were taken into account. Several longitudinal studies of driving in late life have reported higher rates of driving cessation among women compared with men.26,27 These studies, however, failed to consider the effect of the competing risk of cessation due to mortality that is higher among men. When the Asset and Health Dynamics Among the Oldest Old analyses were limited to those surviving the 2-year period of follow-up, women were 3 times more likely to report driving cessation.
A comparison of the men’s and women’s driving life expectancies with total life expectancies found that subsequent to driving cessation, men will have about 6 years of dependency on alternative sources of transportation, compared with about 10 years of dependency for women. The total life expectancies of male and female drivers aged 70 to 74 years in this cohort were approximately 17.7 years and 20.6 years, respectively. For the entire Asset and Health Dynamics Among the Oldest Old cohort, the estimated total life expectancies for men and women in this age group were 15.4 years and 17.4 years, respectively. These differences reflect the poorer health of the men and women in the cohort who were not driving at the time of the baseline survey. In contrast, the total life expectancies for the entire Asset and Health Dynamics Among the Oldest Old cohort exceed the published US population life expectancies of 12.2 years for men and 15.3 years for women, because this cohort was designed to represent only community-dwelling persons aged 70 years and older.28
In the United States, nearly 5% of the population aged 65 years and older resides in nursing homes and other long-term care facilities.29 Similar to the total life expectancy gains over the years stemming from reduced mortality rates in the elderly population, recent reports of reduced rates of physical and cognitive impairments in the elderly population may contribute to future gains in driving life expectancy.1,30,31 Whether increases in driving life expectancy will occur at a faster rate than gains in total life expectancy and thus reduce the average number of years of transportation dependency will require further investigation.
Studies of crashes and driving cessation among elderly drivers have shown stronger associations with measures of visual, physical, and cognitive functioning than with diagnoses of specific medical conditions and diseases.8,10,26,31–33 The Asset and Health Dynamics Among the Oldest Old study indicated that poor vision, limitations in ADLs, poor memory, and depressed mood were independent risk factors for cessation and that, with the exception of depressed mood, the point estimate for the risk of quitting was higher among persons with chronic limitations, compared with those who had more recent incident limitations or with those who had former limitations or no limitations. In contrast, the duration of depressed mood was inversely related to driving cessation, because persons with incident depressed mood were more likely than either those with chronic depressed mood or those with neither chronic nor incident depressed mood to have quit driving. This finding is consistent with that of another study in which persons who quit driving were more likely than those who continued driving to subsequently report symptoms of depressed mood.11 However, the precise temporal relation between the onset of problems with vision, ADLs, memory, and depressive symptoms and driving cessation over the 2-year period of follow-up in this study is unknown because of a lack of information about specific dates of onset.
Several other limitations are notable in this analysis of the Asset and Health Dynamics Among the Oldest Old study data. The calculation of driving life expectancy assumed that driving cessation was an absorbing state with no possibility for resumption of driving, an assumption that may not be true. Some of the nondrivers at baseline who were excluded may have started to drive during the course of the follow-up either as newly licensed drivers or as medically rehabilitated drivers. These contributions, however, would not likely be large enough to substantially alter the driving life expectancy estimates, because fewer than 3% of the nondrivers in this cohort reported driving at follow-up. In addition, the exclusion of nondrivers from the analyses results in an underestimation of years of transportation dependency for the total population aged 75 years and older, especially among women, because only 55% were driving in this age group, compared with 82% of the men. Developing a complete life table approach that took into account both increments and decrements in time spent driving over a specific period and included nondrivers at baseline would require more frequent and detailed follow-up surveys than the 2 waves of data used in this analysis.
The strengths of the Asset and Health Dynamics Among the Oldest Old study include its relatively large sample size of nearly 5000 drivers and its representativeness of older drivers nationwide. Although the sample was large and generally allowed for reliable estimates of age- and sex-specific rates of mortality and driving cessation within the cohort, the estimated total life expectancy for the female drivers had greater variability across sampling strata and was less reliable than either their driving life expectancy or the total life expectancy and driving life expectancy for the male drivers. Studies with larger sample sizes or more waves of follow-ups could potentially yield more reliable total life expectancy and driving life expectancy estimates than those based on this longitudinal study.
In summary, each year hundreds of thousands of older drivers across the country must face the reality of driving cessation and of becoming transportation dependent. This significant life event has been overlooked in much of the literature as a routine consequence of the aging process, particularly in the construct of instrumental ADLs assessing, for example, the ability to prepare meals, shop, use the telephone, manage money, and take medications.34–36 Among the oldest old (persons aged 85 years or older), the difference between driving and not driving typically reflects a sharp contrast in level of physical fitness and mental functioning.37 Although many of the women in this current generation of the Asset and Health Dynamics Among the Oldest Old cohort never drove, younger women in the emerging "baby boom" cohort are as likely to be licensed to drive as men.
Hence, it is appropriate to regard driving as a pervasive task of independence for both men and women that is subject to change in late life in association with age-related changes in health and functioning. The health and social consequences of driving cessation need to be recognized and addressed by health professionals, transportation planners, and policymakers. Failure to fully recognize the magnitude and importance of this transition among elderly adult drivers will compromise goals of improving the quality of life in old age, both now and in the foreseeable future.
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Acknowledgments |
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Human Participant Protection
This study was approved by the institutional review board of the University of Michigan School of Medicine.
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Footnotes |
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Accepted for publication November 6, 2001.
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References |
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
